Bayesian and likelihood approaches to inference have a number of points of close contact, especially from an asymptotic point of view. Both approaches emphasize the construction of interval estimates of unknown parameters. Empirical Bayes methods have historically emphasized instead the construction of point estimates. In this volume researchers present recent work on several aspects of Bayesian, likelihood and empirical Bayes methods, presented at a workshop held in Montreal, Canada. The goal of the workshop was to explore the linkages among the methods, and to suggest new directions for...

In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and...

This monograph is designed to help researchers understand the theory of the design of experiments, so they can easily adapt general principles to their specialty. It seeks to bring the theory to non-statisticians at a reasonable mathematical level so that they can apply and adapt the designs. The authors use self-contained chapters, illustrated applications, and examples to teach the ideas and concepts needed for sound experiment design, and consequently, analysis. The volume includes extensive bibliographic notes and a detailed account of S-Plus.